Very High Spatial Resolution Optical Imagery: Tree-Based Methods and Multi-temporal Models for Mining and Analysis

This chapter presents a selection of image representation methods for very high spatial resolution optical data acquired by agile satellite platforms. Each method aims at specific properties of the image information content and can be tailored to address unique features in spatial, temporal, and angular acquisitions. Techniques for the identification and characterization of surface structures and objects often employ spatial and spectral features best represented in panchromatic and multispectral images, respectively. In both cases, the vastness of the data space can only be addressed effectively by means of some data representation structure that organizes the image information content in meaningful ways. The latter suggest that a globally optimal representation of the object(s) of interest can be obtained through interactions with a scale space as opposed to single-scale information layer. Two examples, the Max-Tree and Alpha-Tree algorithms, are discussed in the context of interactive big data information mining. Optical and structural properties of the surface materials can be exploited by analyzing the tempo-angular domain by means of anisotropic decompositions, which rely on the availability of surface reflectance data and dense angular sampling. Bidirectional reflectance distribution functions of various materials are discussed in detail, showing that the temporal information should always be coupled to the corresponding angular component to make the best use of the available imagery.